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SAS Viya by Xiangxiang Meng, Kevin D. Smith

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Chapter 9: Modeling Categorical Variables

Logistic Regression

Decision Trees

Gradient Boosting, Forests, and Neural Networks

Conclusion

In Chapter 8, we introduced linear regressions, generalized linear models and regression trees for modeling continuous response variables. In this chapter, we focus on applications in which the response variable is categorical, such as organic food that is purchased in a supermarket (Bought, Not), blood press status (High, Normal, Low), and credit card application status (Accepted, Rejected). Logistic regressions and decision trees are introduced in the first two sections for a binary response variable, which is a response variable with only two qualitative outcomes. In the last section, we introduce random forests, ...

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